An Implicit Representation of Chordal Comparabilty Graphs in Linear-Time. - Andrew R. Curtis; Clemente Izurieta; Benson L. Joeris; Scott M. Lundberg; Ross M. McConnell (WG, 2006)

Top down image segmentation using congealing and graph-cut. - Douglas Moore; John Stevens; Scott M. Lundberg; Bruce A. Draper (ICPR, 2008)

O(m logn) Split Decomposition of Strongly Connected Graphs. - Benson L. Joeris; Scott M. Lundberg; Ross M. McConnell (Graph Theory, Computational Intelligence and Thought, 2009)

Analysis of CBRN sensor fusion methods. - Scott M. Lundberg; Randy C. Paffenroth; Jason Yosinski (FUSION, 2010)

An implicit representation of chordal comparability graphs in linear time. - Andrew R. Curtis; Clemente Izurieta; Benson L. Joeris; Scott M. Lundberg; Ross M. McConnell (Discret. Appl. Math., 2010)

O(mlogn) split decomposition of strongly-connected graphs. - Benson L. Joeris; Scott M. Lundberg; Ross M. McConnell (Discret. Appl. Math., 2010)

CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise. - Naozumi Hiranuma; Scott M. Lundberg; Su-In Lee (BCB, 2016)

An unexpected unity among methods for interpreting model predictions. - Scott M. Lundberg; Su-In Lee (CoRR, 2016)

A Unified Approach to Interpreting Model Predictions. - Scott M. Lundberg; Su-In Lee (NIPS, 2017)

Consistent feature attribution for tree ensembles. - Scott M. Lundberg; Su-In Lee (CoRR, 2017)

Checkpoint Ensembles: Ensemble Methods from a Single Training Process. - Hugh Chen; Scott M. Lundberg; Su-In Lee (CoRR, 2017)

Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning. - Gabriel G. Erion; Hugh Chen; Scott M. Lundberg; Su-In Lee (CoRR, 2017)

Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. - Hugh Chen; Scott M. Lundberg; Su-In Lee (CoRR, 2018)

Consistent Individualized Feature Attribution for Tree Ensembles. - Scott M. Lundberg; Gabriel G. Erion; Su-In Lee (CoRR, 2018)

Explainable AI for Trees: From Local Explanations to Global Understanding. - Scott M. Lundberg; Gabriel G. Erion; Hugh Chen; Alex J. DeGrave; Jordan M. Prutkin; Bala Nair; Ronit Katz; Jonathan Himmelfarb; Nisha Bansal; Su-In Lee (CoRR, 2019)

Learning Explainable Models Using Attribution Priors. - Gabriel G. Erion; Joseph D. Janizek; Pascal Sturmfels; Scott M. Lundberg; Su-In Lee (CoRR, 2019)

Explaining Models by Propagating Shapley Values of Local Components. - Hugh Chen; Scott M. Lundberg; Su-In Lee (CoRR, 2019)

Explainable Machine Learning for Science and Medicine. - Scott M. Lundberg (2019)

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. - Rich Caruana; Scott M. Lundberg; Marco Túlio Ribeiro; Harsha Nori; Samuel Jenkins (KDD, 2020)

Understanding Global Feature Contributions With Additive Importance Measures. - Ian Covert; Scott M. Lundberg; Su-In Lee (NeurIPS, 2020)

Deep Transfer Learning for Physiological Signals. - Hugh Chen; Scott M. Lundberg; Gabriel G. Erion; Jerry H. Kim; Su-In Lee (CoRR, 2020)

Understanding Global Feature Contributions Through Additive Importance Measures. - Ian Covert; Scott M. Lundberg; Su-In Lee (CoRR, 2020)

True to the Model or True to the Data? - Hugh Chen; Joseph D. Janizek; Scott M. Lundberg; Su-In Lee (CoRR, 2020)

Feature Removal Is a Unifying Principle for Model Explanation Methods. - Ian Covert; Scott M. Lundberg; Su-In Lee (CoRR, 2020)

From local explanations to global understanding with explainable AI for trees. - Scott M. Lundberg; Gabriel G. Erion; Hugh Chen; Alex J. DeGrave; Jordan M. Prutkin; Bala Nair; Ronit Katz; Jonathan Himmelfarb; Nisha Bansal; Su-In Lee (Nat. Mach. Intell., 2020)

Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. - Jiaxuan Wang; Jenna Wiens; Scott M. Lundberg (AISTATS, 2021)

Model-Agnostic Explainability for Visual Search. - Mark Hamilton; Scott M. Lundberg; Lei Zhang 001; Stephanie Fu; William T. Freeman (CoRR, 2021)

Explaining a Series of Models by Propagating Local Feature Attributions. - Hugh Chen; Scott M. Lundberg; Su-In Lee (CoRR, 2021)

Summarize with Caution: Comparing Global Feature Attributions. - Alex Okeson; Rich Caruana; Nick Craswell; Kori Inkpen; Scott M. Lundberg; Harsha Nori; Hanna M. Wallach; Jennifer Wortman Vaughan (IEEE Data Eng. Bull., 2021)

Explaining by Removing: A Unified Framework for Model Explanation. - Ian Covert; Scott M. Lundberg; Su-In Lee (J. Mach. Learn. Res., 2021)

Improving performance of deep learning models with axiomatic attribution priors and expected gradients. - Gabriel G. Erion; Joseph D. Janizek; Pascal Sturmfels; Scott M. Lundberg; Su-In Lee (Nat. Mach. Intell., 2021)

Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. - Hugh Chen; Scott M. Lundberg; Gabriel G. Erion; Jerry H. Kim; Su-In Lee (npj Digit. Medicine, 2021)

Adaptive Testing and Debugging of NLP Models. - Marco Túlio Ribeiro; Scott M. Lundberg (ACL (1), 2022)

Fixing Model Bugs with Natural Language Patches. - Shikhar Murty; Christopher D. Manning; Scott M. Lundberg; Marco Túlio Ribeiro (EMNLP, 2022)

Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning. - Mark Hamilton; Scott M. Lundberg; Stephanie Fu; Lei Zhang 001; William T. Freeman (ICLR, 2022)

Adaptive Testing of Computer Vision Models. - Irena Gao; Gabriel Ilharco; Scott M. Lundberg; Marco Túlio Ribeiro (ICCV, 2023)

ART: Automatic multi-step reasoning and tool-use for large language models. - Bhargavi Paranjape; Scott M. Lundberg; Sameer Singh 001; Hannaneh Hajishirzi; Luke Zettlemoyer; Marco Túlio Ribeiro (CoRR, 2023)

Sparks of Artificial General Intelligence: Early experiments with GPT-4. - Sébastien Bubeck; Varun Chandrasekaran; Ronen Eldan; Johannes Gehrke; Eric Horvitz; Ece Kamar; Peter Lee; Yin Tat Lee; Yuanzhi Li; Scott M. Lundberg; Harsha Nori; Hamid Palangi; et al. (CoRR, 2023)

Algorithms to estimate Shapley value feature attributions. - Hugh Chen; Ian C. Covert; Scott M. Lundberg; Su-In Lee (Nat. Mac. Intell., 2023)

Sequential Diagnosis with Language Models. - Harsha Nori; Mayank Daswani; Christopher Kelly; Scott M. Lundberg; Marco Túlio Ribeiro; Marc Wilson; Xiaoxuan Liu; Viknesh Sounderajah; Jonathan Carlson; Matthew P. Lungren; Bay Gross; Peter Hames; et al. (CoRR, 2025)